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Additional file 1: Figure S1. Effect of endocytosis inhibitors on FLQY2-SD uptake by HCT 116 and MIA PaCa-2 cells. Table S1. Pharmacokinetic parameters in rat tissues after oral administration of 4 mg/kg FLQY2-SD. Table S2. Parameters of the metabolites detected using ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry in ESI −. Table S3. Parameters of the metabolites detected using ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry in ESI +. Figure S2. (A) Extracted ion chromatograms of FLQY2 (M0) in ESI +, (B) MS/MS spectrum, and the proposed fragmentation pathway of FLQY2 Figure S3. MS/MS spectrum and the proposed fragmentation pathways of (A) M1, (B) M2, (C) M3, (D) M4, (E) M5, (F) (G) M6 or M7 Figure S4. MS/MS spectrum and the proposed fragmentation pathways of (A) M8, (B) (C) M9 or M10, (D) M11 and M12, (E) M13, and (F) M14.

Additional file 1. Additional Figures and Tables.

Objective: The single-agent therapy was unable to provide an effective control of the malignant process, a well-established strategy to improve the efficacy of antitumor therapy is the rational design of drug combinations aimed at achieving synergistic effects. Objective: The objective of this study is generating the new potential anticancer agents with synergistic activity. Owing to the unique mechanism of action of Camptothecin (CPT), it has shown abroad spectrum of anti-cancer activity against human malignancies, and growing evidence revealed that Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) reduce the risk of different kinds of cancers. So four CPT-NSAIDs conjugates were synthesized and evaluated. Methods: In this study, a series of novel CPT - NSAIDs derivatives were synthesized by esterification. These new compounds were evaluated for in vitro antitumor activity against tumor cell lines A549, Hela, HepG2, HCT116 by MTT assay. To probe the required stabilities as prodrugs, stability tests were studied in human plasma. To further evaluate the stability of Ketoprofen-CPT in vivo, the female SD rats were used to determine the pharmacokinetics following a single oral dose. Results: In vitro results showed that Ketoprofen-CPT and Naproxen-CPT conjugates possessed nice efficacy. In a molecular docking model, the two conjugates interacted with Topo I-DNA through hydrogen bonds, - stacking and so on.In human plasma results showed that the prodrug was converted to ketoprofen and another compound. The female SD rats were used to determine the pharmacokinetics following a single oral dose, the half-life (t1/2) of Ketoprofen-CPT was approximately 12 h which was much longer than that of CPT. Conclusion: Good activity was noted for some compounds will be helpful for the design of dualaction agents with most promising anti-cancer activity.

In recent years, streptococcal diseases have severely threatened the development of tilapia aquaculture, but effective prevention and control methods have not yet been established. To understand the immune responses of vaccinated Nile tilapia (Oreochromis niloticus), digital gene expression (DGE) technology was applied in this study to detect the gene expression profile of the Nile tilapia (O. niloticus) liver in response to ScpB (Streptococcal C5a peptidase from group B Streptococcus, ScpB) vaccination and a Streptococcus agalactiae-challenge. The control and the ScpB-vaccinated Nile tilapia yielded a total of 25,788,734 and 27,088,598 clean reads, respectively. A total of 1234 significant differentially expressed unigenes were detected (P 0.05), of which 236 were significantly up-regulated, and 269 were significantly down-regulated (P 0.05, |fold|2, FDR0.05). Of the differentially expressed gene, the identified genes which were enriched using databases of GO and KEGG could be categorized into a total of 67 functional groups and were mapped to 153 signaling pathways including 15 immune-related pathways. The differentially expressed genes (TLR1, TLR2, TLR3, TLR5, TLR9, MyD88, C3, IL-1β, IL-10) were detected in the expression profiles, and this was subsequently verified via quantitative real-time PCR (qPCR). The results of this study can serve as a basis for future research not only on the molecular mechanism of S. agalactiae invasion, but also on the anti-S. agalactiae mechanism in targeted tissues of Nile tilapia.

The advances of sensor technology enable people to monitor air quality through widely distributed low-cost sensors. However, measurements from these sensors usually encounter high biases and require a calibration step to reach an acceptable performance in down-streaming analytical tasks. Most existing calibration methods calibrate one type of sensor at a time, which we call single-task calibration. Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance. In this paper, we propose a multi-task calibration network (MTNet) to calibrate multiple sensors (e.g., carbon monoxide and nitrogen oxide sensors) simultaneously, modeling the interactions among tasks. MTNet consists of a single shared module, and several task-specific modules. Specifically, in the shared module, we extend the multi-gate mixture-of-experts structure to harmonize the task conflicts and correlations among different tasks; in each task-specific module, we introduce a feature selection strategy to customize the input for the specific task. These improvements allow MTNet to learn interaction information shared across different tasks, and task-specific information for each calibration task as well. We evaluate MTNet on three real-world datasets and compare it with several established baselines. The experimental results demonstrate that MTNet achieves the state-of-the-art performance., Comment: 9 pages, 6 figures

A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research. Considering large-scale tabular data in online and offline documents, automatic table recognition has attracted increasing attention from the document analysis community. Though human can easily understand the structure of tables, it remains a challenge for machines to understand that, especially due to a variety of different table layouts and styles. Existing methods usually model a table as either the markup sequence or the adjacency matrix between different table cells, failing to address the importance of the logical location of table cells, e.g., a cell is located in the first row and the second column of the table. In this paper, we reformulate the problem of table structure recognition as the table graph reconstruction, and propose an end-to-end trainable table graph reconstruction network (TGRNet) for table structure recognition. Specifically, the proposed method has two main branches, a cell detection branch and a cell logical location branch, to jointly predict the spatial location and the logical location of different cells. Experimental results on three popular table recognition datasets and a new dataset with table graph annotations (TableGraph-350K) demonstrate the effectiveness of the proposed TGRNet for table structure recognition. Code and annotations will be made publicly available., Comment: Accepted to ICCV 2021

Fasteners that hold rails in a fixed position are essentially important infrastructure components, and abnormal fasteners may cause a train to derail. The periodical inspection of fasteners is a significant guarantee for the safety of railway operation, and machine vision systems are popularly applied for fastener inspection. In this paper, we present an end-to-end abnormal fastener detection method, which identifies abnormal fasteners from a track image that contains a rail, fasteners, sleepers, etc. The proposed method is inspired from the well-established Faster R-CNN, but customizes with two aspects according to the application of fastener inspection. On the one hand, a light-weight backbone network is employed instead of complicated network to quicken detecting speed, and a threshold pruning algorithm is designed to reduce false positive rate. On the other hand, a hybrid loss function, combining weighted softmax loss function with center loss function, is devised to handle both the problems of class imbalance and small inter-class differences. Furthermore, two data synthesis methods are brought forward to solve the small sample problem. The proposed method is verified on a real-world image set of railway inspection, and the experimental results demonstrate that our method outperforms four established baselines.

Streptococcus agalactiae is an important pathogen associated with various aquatic animals, especially tilapia. Streptococcosis has greatly limited the healthy development of tilapia aquaculture in recent times. The development of novel effective vaccines is important for the prevention and control of streptococcosis in fish. We previously constructed a non-encapsulated S. agalactiae strain △cps by the in-frame deletion method. Here, we evaluated whether this mutant △cps is safe for tilapia and suitable for protection against streptococcosis. We observed that the △cps strain was non-pathogenic to tilapia, and there was no reversion of virulence when it was passaged in tilapia. Moreover, the △cps strain survived for at least 11 d in the main immune organs of tilapia. The tilapia vaccinated via intraperitoneal (IP) injection with △cps strain induced a high antibody titer, and the IgM antibody levels were significantly higher in the vaccinated group than in the control group. The vaccination protected tilapia against the S. agalactiae challenge with a relative percent survival of 90.47%. In addition, tilapia immunized with the △cps strain showed significantly higher expression level of IFN-γ, IL-1β, MyD88, IgM, and MHC-Iα in the head kidney than those in the control during the entire observation period. The expression of MHC-IIβ was inhibited during 1–7 d of immunization. These results revealed that the △cps strain is able to induce humoral and cell-mediated immune response in tilapia. Therefore, the strain △cps has a broad application prospect as a target for attenuation in vaccine development.

The goal of this study was to investigate the protective effect of recombinant human EPO(rhEPO) on cerebral microvascular endothelial cells and the mechanisms by which rhEPO interacts with TJs proteins, claudin-5, Occludin and ZO-1 during the early period following traumatic brain injury.Rats (n = 81) were randomly divided into sham-operated group, TBI group and rhEPO+TBI group. Traumatic brain injury was induced by the Marmarou method.Rats were killed at 3, 24, 72 and 168 hours after TBI. The integrity of the blood-brain barrier was investigated by using a spectrophotometer to assess extravasation of Evans blue dye. The expression of Claudin-5, Occludin and ZO-1 were determined by immunohistochemistry and real-time fluorescence quantitative PCR.From 3 hours to 3 days, rats in the TBI group demonstrated a remarkable increase in Evans blue content in the brain, relative to rats in the sham-operated group (p0.05). The expression of Claudin-5 and Occludin was significantly lower than those in the sham-operated group (p0.05). In contrast, rats in the TBI+rhEPO group demonstrated a significant decrease in brain levels.It was found that administration of rhEPO protected cerebral microvascular endothelial cells and reduced permeability of BBB and the mechanisms may be due to increasing the expression of TJs proteins.

In order to find an effective immune preparation to control tilapia streptococcus disease, the Streptococcus agalactiae surface protein serine protease C5a peptidase (ScpB) was cloned and the recombinant protein was encapsulated in poly (lactide-co-glycolic acid) (PLGA) microspheres, which were comprised of biodegradable materials. The ScpB-PLGA vaccine was then administered to the tilapia intraperitoneally at different concentrations, with PBS used as a control, and the relative percent survival (RPS) of each group was calculated. Serum lysozyme and superoxide dismutase (SOD) activity levels and antibody levels (OD450nm) were tested weekly for the duration of the experiment. The results showed that the ScpB loading rate in the PLGA microspheres was 2.55% and the encapsulation efficiency reached 48.76%. The RPS ranged from 66.80% to 87.66%, with the highest RPS noted in group P1 (1 μg/g). The serum lysozyme, SOD and antibody (IgM) levels were significantly higher in the vaccinated fish relative to the control groups (P < 0.01). These results showed that PLGA could serve as an effective adjuvant for a ScpB vaccine and could provide relatively sustained immune protection.

Paper-based medical documents are still widely used in many countries, while the contents within are difficult for patients to store and manage. In contrast, electronic medical documents not only help solve these problems, but also promote the development of telemedicine and medical big data. Thus, how to transform traditional printed medical documents into electronic ones becomes a key issue. It is worth noting that recognizing Chinese medical document in image form is a challenging task, as there are a variety of characters and symbols, including Greek alphabets, mathematical symbols and so on. The structure of Chinese characters is also often intricate. At present, the popular Optical Character Recognition methods are designed for single-scale characters, which tend to have poor performance in those complex scenarios. Based on Convolutional Recurrent Neural Network (CRNN), this paper proposes a multiscale architecture to recognize multi-lingual characters. To verify the effectiveness, the model is trained on a synthetic dataset and evaluated on a real Chinese medical document dataset. The experimental results demonstrate that the proposed method achieves substantial improvement over the recent methods.

Medical laboratory report is one kind of essential document for health care professionals in patient assessment, diagnosis, and long-term monitoring. Compared with paper files, electronic records are convenient for keeping up to date, complete, and accurate, which is already common in modern medical system. But the recognition from historical medical laboratory reports is still in great needs, especially in developing countries. In this paper, we present a document image processing system used for extracting information from medical laboratory reports. Given an image of medical laboratory report, its table areas and texts are firstly segmented following a top-down pipeline. Then, recognition is undergoing for every text that may contain Arabic numerals, mathematical symbols, and multilingual characters. We evaluate the system on a new dataset of medical laboratory reports that includes scanned images and camera-captured images. Our experiments demonstrate that the proposed system can effectively segment the medical document according to its layout and recognize the texts mixed with multi-type characters and symbols to obtain information from medical laboratory reports. The proposed system and the public dataset will benefit the remote healthcare in developing countries.

Rail head surface defect detection is a major issue for rail maintenance, which is mainly used to avoid railway accidents due to rail track failures. The aim of this paper is to present a new vision based inspection technique for detecting special Rolling Contact Fatigue (RCF) defects that particularly occur on rail head surface, meanwhile, an automatic detecting system is implemented, which consists of pre-processing, defect locating, defect identifying and post-processing subsystems. To realize the defect locating sub-procedure, a simple and fast algorithm has been proposed, which adopts geometrical analysis directly on a gray-level histogram curve (the first-order statistical texture property) of the smoothed rail head surface image. Experimental results show that the proposed algorithm has a higher precision and is more suitable than the baseline method for real-time rail head surface defect detection application.